Large scale, dynamically changing sensor networks are used for remote sensing and decision making in pervasive environments like industrial plants, aircraft interiors, battlefields, houses and commercial establishments. Such systems require consistently high performance over a long period in order to avoid costs associated with maintenance. Hence, features like miniaturization, optimal power consumption, data processing capability using minimal resources and adaptability are essential features of sensors used in smart sensor networks.
The exact demands of such a network vary with each scenario in which the network operates based on functionality and usage. The sensors may be dedicated in performing a specific function continuously over long periods of time, e.g. temperature sensors, sound sensors, etc. Or the sensors may be multi-modal sensors, capable of sensing multiple data types, e.g. an integrated sensor for temperature, sound, and humidity. These sensors may be queried either for continuous monitoring or for a specific service request. The arrangement of the sensor network also plays a role in network requirements. While some networks may be organized and systematic, this cannot be assumed. Many sensor networks may be deployed in a seemingly random fashion near the event to be observed. Also, the topology of the sensor network may be subjected to frequent changes, due to removal or addition of sensor nodes and relocation of sub-networks. The environment in which the network is deployed will greatly determine the amount of maintenance available, with repairs in hostile environments being very costly. In such scenarios, the sensor network lifespan has to be maximized using methods for self-organization, power aware communication, and co-operative data processing.
One current approach to a self-organizing network is based on an ant-algorithm. This method is modeled on the observed behavior of ants in finding the best path, e.g. to a food source. The ants make decisions as to which way to travel based on pheromones left by other ants. Thus, a more traveled path is reinforced as more and more ants choose that path. At the same time, longer paths will be used less and less, and the pheromone will ultimately evaporate. The eventual path will consistently be the best, because shorter paths are traveled more frequently in the constant coming and going of a large number of ants. In order to implement such a system in a sensor network, message carriers deposit markers as they are passed along the network. Paths containing these markers are more likely to be chosen by later carriers, and thus are reinforced over time as more markers are deposited. In this way the best path is eventually chosen. In a trained sensor network, this generally results in a fast response to any request for information.
Directed diffusion is a similar approach. The method involves spreading interests for named data across a network. As this is done, gradients are established within the network between nodes. Each gradient is a connection between nodes at a given transmission rate. Initially, the rate is relatively low. Paths that deliver an event first are reinforced by creating high data rate transmissions between nodes in the path.
These solutions to sensor network organization have the disadvantage of overburdening particular portions of the network. Because the same path is being used consistently, those sensors in that path are likely to die out well before other sensors. When these sensors fail, the efficiency of communication across the entire network may degrade. This may result in either unsatisfactory performance or the need to perform costly repairs, possibly replacing large portions of the network. Thus, there is a need for sensor networks that can more effectively balance the factors of performance and sensor life.